The rapid advancement in Digital Economy, along with the proliferation of smart phones and smart devices, has unleashed a magnitude of opportunities to the Enterprises by enhancing connectivity in the new age digitally empowered customers. The Enterprises are leveraging cutting edge digital technologies to drive customer engagement on a dynamic basis and perform pro-active performance management. For example, Autonomous Recommendation systems may be built to generate dynamic personalized products/services offerings to the customers, based on real-time information regarding the customer and his environment/surroundings. Such systems enable the Enterprise to automate the process of identifying the Customer's needs, interests and preferences based on his transaction history, thus improving the revenues and generating profits for the enterprise, while luring the customers to purchase products and services, personalized as per their specific requirements. Recommendation systems are broadly used both in e-commerce and offline retailing. Further, such Recommendation systems span across various sectors from retail to telecom to healthcare and others.
In the current technology landscape, some of the models frequently used in the Recommendation systems are Collaborative filtering approach, content-based filtering approach or a Hybrid approach comprising a combination of the two. In case of collaborative filtering, the recommendation may be generated based on the similarity measure of the consumers and/or products determined by users' rankings, likes/dislikes of products, etc. On the other hand, ‘content based filtering’ captures the attributes or feature sets of the products and maps it to the user preferences while generating recommendations.
However, as the Enterprises evolve, the Customer expectations have also shifted from ‘how effectively your products and services meet my need’ to ‘sense my need before it is felt, engage before need manifests’. They prefer the Enterprises that serve to build an engaging experience rather than focusing purely on product features, performance and efficiency.